Bibliographic record
Abstract
BACKGROUND: Pyogenic liver abscess (PLA) secondary to perforated appendicitis can be potentially life-threatening. However, the mortality of PLA has decreased due to earlier and improved diagnosis, development of intensive care, and progression of minimally invasive treatment. Here we present a patient with a pyogenic liver abscess formed after appendectomy. CASE REPORT: A 40-year-old man complained of abdominal pain in the right upper quadrant for the last month. Examination of his abdomen showed enlarged liver, approximately 3 fingers below costae. Laboratory results showed an increased white blood count of 14.000 /mL and slight hypoalbuminemia of 2.9 g/dL. An ultrasound showed liver abscess. The abdominal CT revealed a 6,78 cm x 6,18 cm x 5,4 cm lobulated hypodense cystic mass. The patient was treated with 3rd generation Cephalosporin and underwent percutaneous drainage with Escherichia coli growth on the culture result. The patient was discharged on the 8th day after admission with significant improvement dan there are no other complications. CONCLUSION: Early diagnosis and prompt treatment can lead to a better prognosis. Antibiotic is recommended in early treatment to lower bacterial load and control the symptoms of systemic infections. However, effective drainage is recognized as the most effective treatment for large liver abscess, since it could lower bacterial load and increase antibiotic penetration into the abscess.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".